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Demand Forecasting Drives Accurate Inventory Predictions

  
  
  

The Gartner IT Glossary explains that, “Demand forecasting applications incorporate historical and predictive customer demand information into production line and sales quotas.”  Sounds simple enough…not really.  Matters become more complex when extending the definition to an end-to-end pull process…still trickier when sensing customer demand and synchronizing supply.

Ultriva’s patent pending Inventory Optimization Tool uncovers opportunities to reduce inventoryInventory Optimization Tool and identify parts for transition kanban replenishment.  The ability to simulate changes in supplier lead time, lot size, and safety stock, ensures that projects can be shared with other planners for viewing purposes and projects can be categorized.

Demand forecasting requires the ability to accurately simulate consumption and compute on-hand inventory for each day.  The formula for these accurate predictions requires three variables:

  • Consumption volume for each day of the interval in the data load
  • Actual on-hand inventory for each day based on current replenishment methodology
  • Projected on-hand inventory for each day based on kanban replenishment methodology

Historical data identifies the potential inventory savings while simultaneously forecasting part shortages. Simulation highlights the potential weakness in current replenishment methodologies.  Typically the Min-Max methodologies used by ERP systems with reorder points, is the primary cause of excess inventory and material shortages.   Since reorder points are not maintained they tend to go out of sync leading to higher inventory on-hand.

In the supply chain, forecasts represent components and purchased parts. For those parts which are dual or multi-sourced Ultriva automatically distributes the gross requirements based on the defined percentage split for that particular part or component.

Manufacturers and Distribution centers need to visually view the gap between consumption and on hand inventory.  This approach is only possible when measuring the variability of actual consumption. The Inventory Optimization Tool (IOT) allows companies to quickly determine which parts are good candidates for kanban replenishment based on potential savings and (S/X, Std Deviation/Mean) variability of consumption. 
 
Estimating Inventory Savings
All inventory savings are not created equally.  Estimated inventory savings per project can be drilled down to savings by part or by supplier.  Data is loaded, the simulation is run, and estimated savings determined.  Similarly, this dynamic forecasting process allows firms to compute safety stock and kanban loop sizing.

Complicating Matters
Ideally manufacturers could establish pull from customers through plants and back to the supply base. This is not practical or feasible because not all purchased parts or components can be on kanban.   The percentage of kanban parts can vary based on the type of manufacturing whether using ETO (Engineered to Order), MTO (Made to Order), MTS (Made to Stock) or a hybrid. Ultriva’s cloud-based supply chain planning and execution software supports manufacturing replenishment by kanban, Virtual kanban, and MRP. Irrespective of the type of replenishment trigger, buyers can communicate the gross requirements (forecasts) to suppliers in real-time providing ideal visibility and supplier collaboration.

Request a free inventory optimization assessment today 
See if you are a candidate for pull-based replacement with a free Inventory Optimization Assessment using Ultriva’s Optimization Tool. 

Analyze your historical consumption and replenishment patterns by item and identify candidate items for pull-based replenishment. The assessment also helps you determine the potential financial impact of deploying supplier ekanban. Sign up for yours today.

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